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Evaluation of the New Information in the ${H}/alpha$ Feature Space Provided by ICA in PolSAR Data Analysis

机译:在PolSAR数据分析中由ICA提供的$ {H} / alpha $特征空间中的新信息的评估

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The Cloude and Pottier H/α feature space is one of the most employed methods for unsupervised polarimetric synthetic aperture radar (PolSAR) data classification based on incoherent target decomposition (ICTD). The method can be split in two stages: the retrieval of the canonical scattering mechanisms present in an image cell and their parameterization. The association of the coherence matrix eigenvectors to the most dominant scattering mechanisms in the analyzed pixel introduces unfeasible regions in the H/α plane. This constraint can compromise the performance of detection, classification, and geophysical parameter inversion algorithms that are based on the investigation of this feature space. The independent component analysis (ICA), recently proposed as an alternative to eigenvector decomposition, provides promising new information to better interpret non-Gaussian heterogeneous clutter (inherent to highresolution SAR systems) in the frame of polarimetric ICTDs. Not constrained to any orthogonality between the estimated scattering mechanisms that compose the clutter under analysis, ICA does not introduce any unfeasible region in the H/α plane, increasing the range of possible natural phenomena depicted in the aforementioned feature space. This paper addresses the potential of the new information provided by the ICA as an ICTD method with respect to Cloude and Pottier H/α feature space. A PolSAR data set acquired in October 2006 by the E-SAR system over the upper part of the Tacul glacier from the Chamonix Mont Blanc test site, France, and a RAMSES X-band image acquired over Brétigny, France, are taken into consideration to investigate the characteristics of pixels that may fall outside the feasible regions in the H/α plane that arise when the eigenvector approach is employed.
机译:Cloude和Pottier H /α特征空间是基于非相干目标分解(ICTD)的无监督极化合成孔径雷达(PolSAR)数据分类的最常用方法之一。该方法可以分为两个阶段:图像单元中存在的规范散射机制的检索及其参数化。相干矩阵特征向量与分析像素中最主要的散射机制的关联在H /α平面中引入了不可行的区域。此约束可能会损害基于此特征空间调查的检测,分类和地球物理参数反演算法的性能。最近提出的独立成分分析(ICA)作为特征向量分解的替代方法,提供了有希望的新信息,可以更好地解释极化ICTD框架中的非高斯异构杂波(高分辨率SAR系统固有的杂波)。不受限于构成分析中的杂波的估计散射机制之间的任何正交性,ICA不会在H /α平面中引入任何不可行的区域,从而增加了上述特征空间中描述的可能自然现象的范围。本文探讨了ICA作为Cloudd和Pottier H /α特征空间的ICTD方法提供的新信息的潜力。 E-SAR系统于2006年10月从法国Chamonix勃朗峰试验场的Tacul冰川上部获取了一个PolSAR数据集,并在法国布雷蒂尼获取了一个RAMSES X波段图像,研究使用特征向量方法时可能落在H /α平面上可行区域之外的像素的特征。

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